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    Rights statement: This is the author’s version of a work that was accepted for publication in Environmental Pollution. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Pollution, 287, 2021 DOI: 10.1016/j.envpol.2021.117620

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Exploring new strategies for ozone-risk assessment: a dynamic-threshold case study

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Article number117620
<mark>Journal publication date</mark>15/10/2021
<mark>Journal</mark>Environmental Pollution
Volume287
Number of pages32
Publication StatusPublished
Early online date19/06/21
<mark>Original language</mark>English

Abstract

Tropospheric ozone is a dangerous atmospheric pollutant for forest ecosystems when it penetrates stomata. Thresholds for ozone-risk assessment are based on accumulated stomatal ozone fluxes such as the Phytotoxic Ozone Dose (POD). In order to identify the effect of ozone on a Holm oak forest in central Italy, four flux-based ozone impact response functions were implemented and tested in a multi-layer canopy model AIRTREE and evaluated against Gross Primary Productivity (GPP) obtained from observations of Eddy Covariance fluxes of CO2. To evaluate if a clear phytotoxic threshold exists and if it changes during the year, six different detoxifying thresholds ranging between 0 and 5 nmol O3 m-2 s-1 were tested.
The use of species-specific rather than more general response functions based on plant functional types (PFT) increased model accuracy (RMSE reduced by up to 8.5%). In the case of linear response functions, a threshold of 1 nmol m-2 s-2 produced the best results for simulations of the whole year, although the tolerance to ozone changed seasonally, with higher tolerance (5 nmol m-2 s-1 or no ozone impact) for Winter and Spring and lower thresholds in Summer and Fall (0-1 nmol m-2 s-1). A “dynamic threshold” obtained by extracting the best daily threshold values from a range of different simulations helped reduce model overestimation of GPP by 213 g C m-2 y-1 and reduce RMSE up to 7.7%. Finally, a nonlinear ozone correction based on manipulative experiments produced the best results when no detoxifying threshold was applied (0 nmol O3 m-2 s-1), suggesting that nonlinear functions fully account for ozone detoxification. The evidence of seasonal changes in ozone tolerance points to the need for seasonal thresholds to predict ozone damage and highlights the importance of performing more species-specific manipulative experiments to derive response functions for a broad range of plant species.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Environmental Pollution. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Pollution, 287, 2021 DOI: 10.1016/j.envpol.2021.117620